MCAoiv | R Documentation |
Multiple Correspondence Analysis with Orthogonal Instrumental Variables
MCAoiv(X, Z, excl = NULL, row.w = NULL, ncp = 5)
X |
data frame with only factors |
Z |
data frame of instrumental variables, which can be numeric or factors. It must have the same number of rows as |
excl |
numeric vector indicating the indexes of the "junk" categories (default is NULL). See |
row.w |
Numeric vector of row weights. If NULL (default), a vector of 1 for uniform row weights is used. |
ncp |
number of dimensions kept in the results (by default 5) |
Multiple Correspondence Analysis with Orthogonal Instrumental Variables consists in three steps :
1. Specific MCA of Y
, keeping all the dimensions of the space
2. Computation of one linear regression for each dimension in the specific MCA, with individual coordinates as response and all variables in X
as explanatory variables.
3. Principal Component Analysis of the set of residuals from the regressions in 2.
An object of class PCA
from FactoMineR
package, with X
as supplementary variables, and an additional item :
ratio |
the share of inertia not explained by the instrumental variables |
.
If there are NAs in Y
, these NAs will be automatically considered as junk categories. If one desires more flexibility, Y
should be recoded to add explicit factor levels for NAs and then excl
option may be used to select the junk categories.
Nicolas Robette
Bry X., 1996, Analyses factorielles multiples, Economica.
Lebart L., Morineau A. et Warwick K., 1984, Multivariate Descriptive Statistical Analysis, John Wiley and sons, New-York.)
wcMCA
, wcPCA
, PCAoiv
library(FactoMineR)
data(tea)
mcaoiv <- MCAoiv(tea[,1:18], tea[,19:22])
mcaoiv$ratio
plot(mcaoiv, choix = "ind", invisible = "ind", col.quali = "black")
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